Disentangling nonrandom structure from random placement when estimating β-diversity through space or time

TY - JOUR

T1 - Disentangling nonrandom structure from random placement when estimating β-diversity through space or time

AU - McGlinn, Daniel J.

AU - Blowes, Shane A.

AU - Dornelas, Maria

AU - Engel, Thore

AU - Martins, Inês S.

AU - Shimadzu, Hideyasu

AU - Gotelli, Nicholas J.

AU - Magurran, Anne

AU - McGill, Brian J.

AU - Chase, Jonathan M.

N1 - Publisher Copyright: © 2025 The Author(s). Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.

PY - 2025/3/18

Y1 - 2025/3/18

N2 - There is considerable interest in understanding patterns of β-diversity that measure the amount of change in species composition through space or time. Most hypotheses for β-diversity evoke nonrandom processes that generate spatial and temporal within-species aggregation; however, β-diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β-diversity. We isolate the effect of within-species spatial or temporal aggregation on beta-diversity using a coverage standardized metric of β-diversity (βC). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α-, γ-, and β-scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta-communities.

AB - There is considerable interest in understanding patterns of β-diversity that measure the amount of change in species composition through space or time. Most hypotheses for β-diversity evoke nonrandom processes that generate spatial and temporal within-species aggregation; however, β-diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β-diversity. We isolate the effect of within-species spatial or temporal aggregation on beta-diversity using a coverage standardized metric of β-diversity (βC). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α-, γ-, and β-scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta-communities.

KW - aggregation

KW - patchiness

KW - rarefaction

KW - sampling effects

KW - scaling

KW - species turnover

KW - species-abundance distribution

UR - http://www.scopus.com/inward/record.url?scp=105000832826&partnerID=8YFLogxK

U2 - 10.1002/ecs2.70061

DO - 10.1002/ecs2.70061

M3 - Article

AN - SCOPUS:105000832826

SN - 2150-8925

VL - 16

JO - Ecosphere

JF - Ecosphere

IS - 3

M1 - e70061

ER -